Implicit Representation and Scene Reconstruction from Probability Density Functions

A technique is presented for representing linear features as probability density functions in two or three dimensions. Three chief advantages of this approach are (1) a unified representation and algebra for manipulating points, lines, and planes, (2) seamless incorporation of uncertainty information, and (3) a very simple recursive solution for maximum likelihood shape estimation. Applications to uncalibrated affine scene reconstruction are presented, with results on images of an outdoor environment.

• Type: Conference
• Year: 1999
• Where: CVPR
• Authors: Steven M. Seitz, P. Anandan
• Keywords: Computer Vision, CVPR 1999, Likelihood Shape Estimation, Probability Density Functions, Simple Recursive Solution, Uncalibrated Affine Scene